Curate data, annotate images, video, 3D point clouds and medical scans with AI, then train, evaluate and deploy models — in the cloud or fully on-premise.
Every stage works on the same data, labels and models, so nothing is exported between tools. And the loop closes: each model you deploy pre-labels the next batch, so every round starts ahead of the last.
Import COCO, YOLO and other formats, or link the files in your S3, GCS or Azure buckets, then search a whole project by meaning.
A dedicated editor for images, video, 3D point clouds, medical volumes, meshes and audio. AI assistants label whole datasets or the routine part of each item, and people fix only what they missed.
Split the work into labeling jobs and queues, and catch errors before they reach a model.
Fine-tune detection, segmentation and classification models on your labels, on your own GPUs.
Every training run ends with an evaluation report, and every error leads to the images behind it.
Connect a server, a cloud machine or a laptop as an agent with one command, and serve any checkpoint on it.
Apply a trained model to fresh data with the Predict App, and the next round of labeling starts from its predictions instead of empty images. Live Training keeps a model learning while your team labels.
Each type of data gets an editor built for it, not an image editor stretched to fit, and AI assistants work in every one of them: they label the routine part, or all of it, and people check the rest.
Fast on the hardest images: crowded scenes, huge scans and imagery beyond RGB. Boxes, polygons, masks, keypoints and every other shape, with AI drafting the routine part and people checking the rest.
From traffic and retail cameras to sports and surgery: AI follows every object through the footage, people step in only where it loses track, and every event gets its tags on the timeline.
LiDAR for autonomous driving, robotics and mapping: single scans or whole drives, next to the cameras that saw them, with AI segmenting the ground and fitting and tracking the cuboids for you.
CT, MRI and other volumes for radiology and research teams: everything a desktop viewer does, with nothing to install, shared with the whole team, and AI that segments as you go.
Draw a box and get a mask, type a class and get every match, or let your own model label first.
Waveform and spectrogram on one playhead, in the same projects and labeling jobs.
When no toolbox fits the task, add your own panels and steps to it in Python with App Engine.
Run Supervisely on your own servers or in your cloud, fully offline if you need to, or let us host a private instance for you, isolated from every other customer. Either way, computer vision engineers work with your team from the first day of the trial.
One server or a Kubernetes cluster, AWS EKS included. Installs and upgrades take one command.
A dedicated Supervisely run by us, isolated from every other customer, when you would rather not run it.
No internet needed: air-gapped networks work, and your data never leaves them.
On your own servers or ours, with an engineer at your side, and more time if you need it.
A private Slack workspace with a dedicated success manager, onboarding and training sessions.
When a feature is missing, we help build it with Supervisely Apps and custom integrations.
Import and export formats, neural networks, data tools and labeling helpers run as apps inside Supervisely. When none fits, App Engine turns a Python script into an app with a web UI.
Connect Claude, Cursor or any other MCP client, and an AI agent works in Supervisely for you: it finds the data, runs your models, sets up labeling jobs and reports back, with your permissions and nothing more. Under it are the open-source Python SDK and the REST API, which reach everything the UI does.
import supervisely as sly
api = sly.Api.from_env()
# Create a project and upload a batch of images
project = api.project.create(WORKSPACE_ID, "Defect inspection")
dataset = api.dataset.create(project.id, "line-04")
api.image.upload_paths(dataset.id, names, paths)
# Pre-label everything with a model deployed in Supervisely
model = api.nn.connect(task_id=MODEL_TASK_ID)
model.predict(project_id=project.id, upload_mode="append")# Every action in the UI is available over the API
curl -X POST \
https://app.supervisely.com/public/api/v3/projects.list \
-H "x-api-key: $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"workspaceId": 42}'“A very high product quality.”
BMW Group uses Supervisely to annotate manufacturing images from production lines in their world-wide plants for enhancing quality inspections using deep learning. Supervisely is integrated into the BMW Group AI Platform in order to empower computer vision based AI use cases.
BMW Group · Manufacturing quality inspection“Immensely grateful for their pivotal role in our work”
Working with Supervisely has significantly enhanced our capability to develop AI models for lung CT scans. What sets Supervisely apart is its exceptional support team who are really responsive and adapt to our unique needs with a range of apps and helper files.
“Helps us to do data scientist work better and faster”
We use Supervisely since 2019. The key advantage of this tool is that Supervisely provides a complete data treatment pipeline. An important advantage is that a Supervisely instance can be deployed autonomously on a Client infrastructure, and distributed on different servers.
“A valuable part of our computer vision operations”
Supervisely is a valuable part of our computer vision operations, enabling our teams to efficiently work with camera and LiDAR data across sensor fusion projects. We use Supervisely for its extensive feature set and its ability to adapt to our changing requirements.
“Extremely satisfied with Supervisely and would highly recommend it”
The review tools have been invaluable in ensuring the quality and accuracy. The Python SDK has also been incredibly helpful in automating and streamlining our workflow. The support team on Slack has been extremely helpful and responsive.
“Supervisely excels at that and much more”
We’ve been exceedingly impressed with the customer support, addition of new features, and the flexibility of the publicly available SDK/API. The Supervisely team has also been fast to respond to support questions, and has shown a lot of openness when given feedback on potential improvements.
Your team next?
Talk to a computer vision engineer about your data, your models and your infrastructure.
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